Chance constraint models, such as (\(Q\) ), are difficult to solve for two main reasons [1]}. First, calculating the probability of meeting a certain constraint is often challenging due to multidimensional integration. Second, the feasibility region defined by the chance constraints may not be convex [2]}. There are two main approaches solve chance constraints models. Both approaches approximate the probabilistic constraints. One approach discretizes the probability distribution function of the random parameters, and solves the corresponding combinatorial problem [3]}, [4]}. The other approach develops convex approximations of the probabilistic constraint [5]}. We follow the first approach to solve (\(Q\) ).
